The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor reaching an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures and possible abnormalities in medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical structures such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures including possible anomalies for further review. Such possible anomalies are often called candidates and are considered to be generated by the CAD system based upon the medical images.
CAD systems are often used to aid visual inspection by radiologists to detect anomalies in medical images. Human visual inspection and analysis of images usually takes from minutes to hours to complete. Due to the poor discrimination of anomalous regions in some images, however, many false alarms or missed detections may result. Current technology provides poor visualization support for this time-consuming and error-prone task.
Therefore, there exists a need to improve image visualization to facilitate anomaly detection.